Slides – http://bit.ly/user16

Why interactive graphics?

Technique Related Question(s) Examples
Identification What is this point/mark? Hover for additional info
Filter How does one group compare to another?
What happened during this time period?		 shiny::selectInput()
shiny::sliderInput()
Click on legend entries
Zoom & pan Is there local structure? Click & drag to alter x/y limits
Linked highlighting How does the marginal/joint compare to a conditional? Linked brushing on a scatterplot matrix
  • Why should presentation graphics be interactive?
    • Helps demonstrate your point
  • Why should exploratory graphics be interactive?
    • Generate insight faster (thus, iteration time is crucial!).

Why web graphics? Portability!

Why web graphics? Composability!

Web graphics usually aren't practical for exploring data

Identification, zoom & pan w/ ggplotly

library(plotly)
p <- ggplot(diamonds, aes(price, carat, color = cut)) + 
  geom_point(alpha = 0.05)
p %>% ggplotly() %>% toWebGL()

Extending James' plot

Linked highlighting via ggplotly

That's great, but…

  • ggplot2's interface wasn't designed for interactive graphics.
  • ggplot2 requires data frame(s) and can be inefficient (especially for time series).
  • plot_ly() provides a low-level R interface to plotly.js (now open source!).
  • Smarter defaults, informative warnings/errors, and higher-level functionality are on their way to plot_ly().

Smart defaults

plot_ly(z = ~volcano)
#> No trace type specified. Applying `add_heatmap()`.
#> Read more about this trace type here -> https://plot.ly/r/reference/#heatmap

Informative warnings/errors

plot_ly(puppies = rnorm(100)) %>% add_boxplot()
#> Error: Must supply either `x` or `y` attribute
plot_ly(x = rnorm(100)) %>% add_boxplot(name = "puppies")

plot_ly loves dplyr verbs

txhousing %>%
  group_by(city) %>%
  plot_ly(x = ~date, y = ~median) %>%
  add_lines()

data-plot-pipeline

txhousing %>%
  group_by(city) %>%
  plot_ly(x = ~date, y = ~median) %>%
  add_lines(name = "Texan Cities") %>%
  filter(city == "Houston") %>%
  add_lines(name = "Houston")

data-model-plot-pipeline

library(broom)
mtcars %>%
    lm(mpg ~ wt, data = .) %>%
    augment() %>%
    mutate(l  = .fitted - 1.96 * .se.fit, u = .fitted + 1.96 * .se.fit) %>%
    plot_ly(x = ~wt, y = ~mpg) %>%
    add_markers(fill = "black") %>%
    add_lines(y = ~.fitted, stroke = "steelblue") %>%
    add_ribbons(ymin = ~l, ymax = ~u, fill = "gray", alpha = 0.3)

split-apply-subplot

s <- split(mpg, mpg$drv)
plots <- lapply(s, function(d) plot_ly(d, x = ~cty, name = ~drv))
subplot(plots, nrows = length(plots), shareX = TRUE)

Thank you!